This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as an addition or alternative to traditional tests that are widely used. This article is written as a tutorial, but it also features an interesting research result in the last section. The example used in this tutorial shows how intuiting can be wrong, and why you need data science.

The expected number of submissions is at least 5 for half day workshops and at least 8 for full day ones. Workshops that do not reach the suggested target might not be activated. Workshop schedule: each workshop will be assigned a number of slots (from 1 to 3 slots) during the conference days. A slot can vary from 1 to 2.5 hours. The distribution of slots will take into account the accepted papers and workshop organization.

Deep learning is the technology driving today's artificial intelligence revolution. Dimensionality reduction is one of the most crucial tools in a data scientists' toolbox, and modern tools can yield truly magical results. ODSC Europe 2017 is a unique collection of over 70 insightful presentations on data science modeling, tools, and languages, and topics delivered by top experts in the field. Topics include deep learning, quant finance and AI for business and more. Data visualisation offers a brilliant way of bringing the raw numbers to life.

When the Arizona Daily Star began experimenting with chatbots in 2016, readers seemed excited…and a little confused. They were fascinated by the new technology, but often responded to the bot in ways that hinted they were unsure what, or who, was on the other end. "A lot of users feel like they're talking to a person," says Daily Star Product Manager Becky Pallack, who helped test one bot targeted at local parents and another for super shoppers. "They'll say thank you and send emojis." Bots are everywhere now, helping people hail Lyfts, order pizza, and choose lipstick--and the experience can range from simple and easy to befuddling and unpleasant.

We present a neurosymbolic approach to the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing highlevel concepts across domains and learning complex procedures are two key challenges in lifelong learning. We show that a combination of gradientbased learning and symbolic program synthesis can be a more effective response to these challenges than purely neural methods. Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language, and decides on the library functions that should be reused and the architectures that should be used to combine them; and (2) a neural module that trains synthesized programs using stochastic gradient descent. We evaluate our approach on three algorithmic tasks. Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning.

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation problems. How to Implement Beam Search Decoder for Natural Language Processing Photo by See1,Do1,Teach1, some rights reserved. In natural language processing tasks such as caption generation, text summarization, and machine translation, the prediction required is a sequence of words.

Voice technology and natural language recognition is at the forefront of AI development that is transforming our everyday life, and will undoubtedly be part of our future. Deep learning is at the core of identifying and processing voice for automation and new innovations. Join this Data Science Central webinar as conversational AI pioneer, Voicebox, shares their journey from an on-premise system that was manually intensive and costly to maintain, to an agile cloud platform that has allowed them to build, schedule, and run multiple production data pipelines that feed into their deep learning models with ease.

Used for object detection and more.

Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images. In the last few years, the field of machine learning has made tremendous progress on addressing these difficult problems. In particular, we've found that a kind of model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks -- matching or exceeding human performance in some domains.

This class is a great overview of the types of machine-learning models, and some of the history of how those models came into use. The fundamental explanations of complex ideas are generally excellent and very clear, but the practical equations that are necessary for implementations are difficult to understand for someone like me who is not used to reading abstract mathematical equations. Examples of equations that are worked out with explicit values are few and far between and this doesn't help. This makes the programming assignments exceptionally difficult even though the code they require is simple. Also, the amount of time required for this course is enormous, easily 10x what is predicted when there is a programming assignment.